spring-ai-integration

# Spring AI Integration

## Dependencies

```xml
<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-bom</artifactId>
            <version>2.0.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <!-- Choose your model provider — pattern is spring-ai-starter-model-<provider> -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-anthropic</artifactId>
    </dependency>
    <!-- OR -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-openai</artifactId>
    </dependency>

    <!-- For RAG / vector search -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-vector-store-pgvector</artifactId>
    </dependency>
    <!-- QuestionAnswerAdvisor lives here — 2.0 renamed spring-ai-advisors-vector-store -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-vector-store-advisor</artifactId>
    </dependency>
</dependencies>
```

> **Version pairing matters.** Spring Boot 4 requires **Spring AI 2.0** (`spring-ai-bom` 2.0.0);
> the 1.x line targets Boot 3 only. Starter coordinates follow `spring-ai-starter-model-<provider>`
> (e.g. `-model-anthropic`, `-model-openai`) and `spring-ai-starter-vector-store-<store>`.
> Agents trained on pre-1.0 Spring AI emit `spring-ai-<x>-spring-boot-starter` — those names
> resolve to nothing in Maven Central. Also gone in 2.0: `spring-ai-starter-model-azure-openai`
> (use the OpenAI starter with an Azure base URL instead).

## ChatClient — Basic Usage

```java
@Service
@RequiredArgsConstructor
public class DocumentSummaryService {

    private final ChatClient chatClient;

    public String summarize(String conversationId, String content) {
        return chatClient.prompt()
            .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
            .user(u -> u.text("Summarize the following document in 3 bullet points:\n\n{content}")
                .param("content", content))
            .call()
            .content();
    }

    // With system prompt
    public String analyzeFinancial(String conversationId, String document, String language) {
        return chatClient.prompt()
            .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
            .system("You are a financial analyst. Respond in {language}.")
            .system(s -> s.param("language", language))
            .user(document)
            .call()
            .content();
    }
}
```

Every call using the configured memory advisor must provide a user- or session-scoped
`ChatMemory.CONVERSATION_ID`. Never use one shared conversation ID for all users.

## ChatClient Bean Configuration

```java
@Configuration
public class AiConfig {

    @Bean
    public ChatMemory chatMemory() {
        // InMemoryChatMemory is long gone. Use MessageWindowChatMemory —
        // it caps history to a sliding window and defaults to an in-memory repository.
        return MessageWindowChatMemory.builder()
            .maxMessages(20)
            .build();
    }

    @Bean
    public ChatClient chatClient(ChatClient.Builder builder, ChatMemory chatMemory) {
        return builder
            .defaultSystem("You are a helpful assistant for an e-commerce platform.")
            .defaultAdvisors(
                MessageChatMemoryAdvisor.builder(chatMemory).build(), // builder, not new(...)
                new SimpleLoggerAdvisor() // logs prompts/responses
            )
            .build();
    }
}
```

```java
// 2.0: the conversation id is REQUIRED on every call that goes through a memory advisor.
// ChatMemory.DEFAULT_CONVERSATION_ID is removed — omitting the param throws IllegalArgumentException.
public String chat(String sessionId, String message) {
    return chatClient.prompt()
        .user(message)
        .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, sessionId))
        .call()
        .content();
}
```

## Prompt Templates (externalized)

```java
// src/main/resources/prompts/analyze-order.st
// Analyze this order and identify any anomalies:
// Customer: {customer}
// Items: {items}
// Total: {total}
// Flag any unusual patterns.

@Service
public class OrderAnalysisService {

    @Value("classpath:prompts/analyze-order.st")
    private Resource promptTemplate;

    public String analyzeOrder(String conversationId, Order order) {
        return chatClient.prompt()
            .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
            .user(u -> u.text(promptTemplate)
                .param("customer", order.getCustomerEmail())
                .param("items", order.getItems().toString())
                .param("total", order.getTotal()))
            .call()
            .content();
    }
}
```

## Structured Output

```java
// Define the target record
public record OrderClassification(
    String category,
    String priority,
    List<String> tags,
    boolean requiresManualReview
) {}

@Service
public class OrderClassifier {

    public OrderClassification classify(String orderDescription) {
        return chatClient.prompt()
            .user("Classify this order: " + orderDescription)
            .call()
            .entity(OrderClassification.class); // Spring AI handles JSON parsing
    }
}
```

## RAG Pipeline

```java
@Configuration
public class RagConfig {

    // No manual VectorStore bean — the spring-ai-starter-vector-store-pgvector
    // starter auto-configures one. Just inject it. (The old `new PgVectorStore(...)`
    // constructor is removed; if you must build one, use PgVectorStore.builder(...).)

    @Bean
    public ChatClient ragChatClient(ChatClient.Builder builder, VectorStore vectorStore) {
        return bui